Neural networks have been successfully applied. However, large-scale neural network parameters make a very high requirement on storage. On one hand, the large-scale neural network parameters require a huge storage capacity. On the other hand, access to massive neural network data may result in huge memory access energy consumption.
At present, a memory storing neural network parameters is an error correcting code (ECC) memory. The ECC memory may correct an error occurring if data is read, but the ECC memory may also bring an additional storage capacity overhead and memory access power consumption overhead. A neural network algorithm has a certain fault-tolerant capability. Storing all parameters of a neural network in an ECC memory ignores fault tolerance of the neural network and results in an additional storage overhead, computation overhead, and memory access overhead. Therefore, how to select a memory suitable for neural network processing in combination with a fault-tolerant capability of a neural network is a problem urgent to be solved.